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Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method
Transcriptomic data of cultured cells treated with a chemical are widely recognized as useful numeric information that describes the effects of the chemical. This property is due to the high coverage and low arbitrariness of the transcriptomic data as profiles of chemicals. Considering the importanc...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006730/ https://www.ncbi.nlm.nih.gov/pubmed/36915410 http://dx.doi.org/10.1093/nargab/lqad022 |
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author | Ishiguro, Hiromu Mizuno, Tadahaya Uchida, Yasuo Sato, Risa Sasaki, Hayate Nemoto, Shumpei Terasaki, Tetsuya Kusuhara, Hiroyuki |
author_facet | Ishiguro, Hiromu Mizuno, Tadahaya Uchida, Yasuo Sato, Risa Sasaki, Hayate Nemoto, Shumpei Terasaki, Tetsuya Kusuhara, Hiroyuki |
author_sort | Ishiguro, Hiromu |
collection | PubMed |
description | Transcriptomic data of cultured cells treated with a chemical are widely recognized as useful numeric information that describes the effects of the chemical. This property is due to the high coverage and low arbitrariness of the transcriptomic data as profiles of chemicals. Considering the importance of posttranslational regulation, proteomic profiles could provide insights into the unrecognized aspects of the effects of chemicals. Therefore, this study aimed to address the question of how well the proteomic profiles obtained using data-independent acquisition (DIA) with the sequential window acquisition of all theoretical mass spectra, which can achieve comprehensive and arbitrariness-free protein quantification, can describe chemical effects. We demonstrated that the proteomic data obtained using DIA-MS exhibited favorable properties as profile data, such as being able to discriminate chemicals like the transcriptomic profiles. Furthermore, we revealed a new mode of action of a natural compound, harmine, through profile data analysis using the proteomic profile data. To our knowledge, this is the first study to investigate the properties of proteomic data obtained using DIA-MS as the profiles of chemicals. Our 54 (samples) × 2831 (proteins) data matrix would be an important source for further analyses to understand the effects of chemicals in a data-driven manner. |
format | Online Article Text |
id | pubmed-10006730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-100067302023-03-12 Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method Ishiguro, Hiromu Mizuno, Tadahaya Uchida, Yasuo Sato, Risa Sasaki, Hayate Nemoto, Shumpei Terasaki, Tetsuya Kusuhara, Hiroyuki NAR Genom Bioinform Standard Article Transcriptomic data of cultured cells treated with a chemical are widely recognized as useful numeric information that describes the effects of the chemical. This property is due to the high coverage and low arbitrariness of the transcriptomic data as profiles of chemicals. Considering the importance of posttranslational regulation, proteomic profiles could provide insights into the unrecognized aspects of the effects of chemicals. Therefore, this study aimed to address the question of how well the proteomic profiles obtained using data-independent acquisition (DIA) with the sequential window acquisition of all theoretical mass spectra, which can achieve comprehensive and arbitrariness-free protein quantification, can describe chemical effects. We demonstrated that the proteomic data obtained using DIA-MS exhibited favorable properties as profile data, such as being able to discriminate chemicals like the transcriptomic profiles. Furthermore, we revealed a new mode of action of a natural compound, harmine, through profile data analysis using the proteomic profile data. To our knowledge, this is the first study to investigate the properties of proteomic data obtained using DIA-MS as the profiles of chemicals. Our 54 (samples) × 2831 (proteins) data matrix would be an important source for further analyses to understand the effects of chemicals in a data-driven manner. Oxford University Press 2023-03-11 /pmc/articles/PMC10006730/ /pubmed/36915410 http://dx.doi.org/10.1093/nargab/lqad022 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Standard Article Ishiguro, Hiromu Mizuno, Tadahaya Uchida, Yasuo Sato, Risa Sasaki, Hayate Nemoto, Shumpei Terasaki, Tetsuya Kusuhara, Hiroyuki Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method |
title | Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method |
title_full | Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method |
title_fullStr | Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method |
title_full_unstemmed | Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method |
title_short | Characterization of proteome profile data of chemicals based on data-independent acquisition MS with SWATH method |
title_sort | characterization of proteome profile data of chemicals based on data-independent acquisition ms with swath method |
topic | Standard Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006730/ https://www.ncbi.nlm.nih.gov/pubmed/36915410 http://dx.doi.org/10.1093/nargab/lqad022 |
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